Spatio-Temporal Variations of Carbon Use Efficiency in Natural Terrestrial Ecosystems and the Relationship with Climatic Factors in the Songnen Plain, China

Round 1

Reviewer 1 Report

Comments

This manuscript deals with Spatio-temporal variations of carbon use efficiency in natural terrestrial ecosystems and the relationship with climatic factors. This study used the Songnen Plain of China as an illustration. The results could provide information concerning Spatio-temporal variations of carbon use efficiency. The comments and suggestions are as following.

Title and Abstract can reflect whole text that are suitable for audiences. I only have a suggestion for Introduction chapter. Since the study site focuses on the Songnen Plain of China. More information of study site or researches concerning this study topic should be emphasized in this chapter because audiences might be not so familiar with this area. The results are sound I have no further suggestions here. In Discussion, “Figure 14: Average CUE variations of natural ecosystems and farmland in the SNP from 2001 to 2015.” is cited from references [32] and [33]? Or do it by authors? Because references [32] and [33] are 2007 and 2009, I suggested giving note in figure tittle as “Figure 14: Average CUE variations of natural ecosystems and farmland in the SNP from 2001 to 2015 and methods by Ai-Min et al. (2007) and Du et al. (2009). Moreover, some limitations of this study should be mentioned in this chapter.

Overall, I believe that this manuscript has contribution in the remote sensing file and I am pleasure to recommend it for publication in the “remote sensing”.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 1 Comments Point 1: Title and Abstract can reflect whole text that are suitable for audiences. I only have a suggestion for Introduction chapter. Since the study site focuses on the Songnen Plain of China. More information of study site or researches concerning this study topic should be emphasized in this chapter because audiences might be not so familiar with this area. Response 1: We appreciate the comments. We added additional information as suggested in in the Introduction section, including: Between revised lines 49-56: the added new sentences and reference citations read as “About 50–70% of the carbon fixation is returned to the environment through Ra [4,5]. Carbon allocation among plant processes (e.g. respiration, biomass production) and organs (e.g. leaves, stem) is a key process in the carbon cycle because it determines the residence time and location of carbon in the ecosystem [6,7]. For example, the residence times of the carbon used for maintenance respiration and the carbon allocated to the structural biomass of organs are drastically different, ranging from a few hours to a few years [6]. Therefore, the allocation process of carbon is highly relevant to understand ecosystem carbon stock and carbon cycles [6].” Between revised lines 62-65; the added new sentences and reference citations as “In practice, GPP usually represents the total amount of carbon captured through photosynthesis, and NPP is the net carbon stored in plant after the reduction of GPP through by plant respiration [1]. CUE is also a measure of how GPP is partitioned into NPP and Ra [7]. Less Ra may result in larger carbon reserve accumulation.” Between revised lines 96-98; the added new sentences as “As one of the key agricultural areas and important grain commodity bases, the SNP has been among designated ecological red-line as protected farmland area in China.” Point 2: In Discussion, “Figure 14: Average CUE variations of natural ecosystems and farmland in the SNP from 2001 to 2015.” is cited from references [32] and [33]? Or do it by authors? Because references [32] and [33] are 2007 and 2009, I suggested giving note in figure tittle as “Figure 14: Average CUE variations of natural ecosystems and farmland in the SNP from 2001 to 2015 and methods by Ai-Min et al. (2007) and Du et al. (2009). Moreover, some limitations of this study should be mentioned in this chapter. Response 2: We appreciate the comments. All the figures presented in this manuscript are resulted from our own research output. We revised the sentences to increase the clarification of the questioned Figure 14 (revised new Figure 15). The sentences between revised Lines 429-434 read as “The ecological and environmental restoration projects, such as the “Three-North Shelterbelt Project” and the “Grain for Green Project” have achieved some positive effects [43, 44]. We used the same method to calculate the CUE of farmland. By comparison, we found that the average CUE of natural ecosystem in SNP showed a similar variation as that of the internal farmland from 2001 to 2015 (Figure 15). The CUE of farmland and natural ecosystem increased simultaneously.” Regarding the limitation of the study, we recognized, in particular, the uncertainty issue when involved with public-domain and coarser spatial resolution remote sensing derived land surface characterization and measurements. We added a sentence and a reference citation in revised Lines 186-187, in Method section under the 2.4 Calculation of CUE. The new sentence reads as “However, uncertainty issue has been recognized by studies using public-domain data, e.g., in water use efficiency (WUE) [44].”

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

I like very much your idea of the research, but the manuscript looks like a report of conducted researches. There is obligatory to have a significant set of improvements:

The Introduction is significantly too general, you need to add much more details oriented on theoretical background, to indicate other solutions of different authors.

The Methods are so generally prepared. There no chance to follow your research algorithm. Please add a research schema, and then to describe all steps in details. There is a missing an assessment of accuracy, validation of used methods.

The Results, you need to start with general conclusions and then to present details.

The Discussion needs a significant improvements. The most important issue is to compare the most important results with outcomes of other researchers.

More details are presented in the attached manuscript.

Best regards

Reviewer

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 2 Comments Point 1: The Introduction is significantly too general, you need to add much more details oriented on theoretical background, to indicate other solutions of different authors. Response 1: We appreciate the comments. We added additional information as suggested. We made sincere efforts to revise the manuscript and hope that the concerns, critiques and suggestions have been addressed to satisfaction in the revised manuscript. We added additional information in the Introduction section, including: Between revised lines 45-47; the added new sentences read as “The GPP is the sum of NPP and autotrophic respiration (Ra), and Ra plus heterotrophic respiration (Rh) comprises ecosystem respiration.” Between revised lines 49-56; the added new sentences and reference citations read as “About 50–70% of the carbon fixation is returned to the environment through Ra [4,5]. Carbon allocation among plant processes (e.g. respiration, biomass production) and organs (e.g. leaves, stem) is a key process in the carbon cycle because it determines the residence time and location of carbon in the ecosystem [6,7]. For example, the residence times of the carbon used for maintenance respiration and the carbon allocated to the structural biomass of organs are drastically different, ranging from a few hours to a few years [6]. Therefore, the allocation process of carbon is highly relevant to understand ecosystem carbon stock and carbon cycles [6].” Between revised lines 62-65; the added new sentences read as “In practice, GPP usually represents the total amount of carbon captured through photosynthesis, and NPP is the net carbon stored in plant after the reduction of GPP through by plant respiration [1]. CUE is also a measure of how GPP is partitioned into NPP and Ra [7]. Less Ra may result in larger carbon reserve accumulation.” Between revised lines 67-69; the added new sentences read as “How efficient of ecosystem could convert GPP into plant and soil storage greatly determines the carbon sequestration of terrestrial ecosystems, so CUE changes strongly affect ecosystem carbon budgets [10].” Between revised lines 73-76; the added new sentences read as “Piao et al. (2010) demonstrated that CUE of different vegetation differed greatly from the south temperate to the tropic ecoregions based on a global forest C-flux database, and found that the spatial patterns of forest annual Ra at the global scale were largely controlled by temperature [12].” Between revised lines 80-85; the added new sentences read as “Tang et al. (2019) established a global database of site-year CUE based on field observations for five ecosystem types and diagnosed the spatial variability of CUE with climate and other environmental factors (e.g. soil variables). Two prominent gradients of CUE in ecosystem types and latitude were found worldwide. CUE varied with ecosystem types, being the highest in wetland and lowest in grassland. CUE decreased with latitude, showing the lowest values in tropics, and the highest CUE were found in higher latitude regions [3].” Between revised lines 88-91; the added new sentences and reference citations read as “From individual plants to an entire ecosystem, phenology regulates directly or indirectly carbon (e.g., photosynthesis and respiration) fluxes between the land surface and the atmosphere [14] through altering physiological and structural characteristics, including photosynthetic rate, canopy conductance and albedo [14-16].” Point 2: The Methods are so generally prepared. There no chance to follow your research algorithm. Please add a research schema, and then to describe all steps in details. There is a missing an assessment of accuracy, validation of used methods. Response 2: We added additional information in the Method section to address the questions, including: Under the 2.2 , we added a new paragraph (Lines 131-138), which read as: “In this study, CUE at the monthly and seasonal scale derived from MODIS data products and the ancillary data were used to explore the spatial-temporal variations of CUE of natural ecosystems and their responses to climate and LSP changes. The main steps are as follows: (1) estimating NPP at monthly scale by the CASA (Carnegie-Ames-Stanford approach) model; (2) calculating monthly CUE of different ecosystems and performing the trend analysis; (3) extracting LSP metrics and analyzing the effects of phenology and climatic factors on the variations of ecosystem CUE by the correlation and partial correlation analysis methods. Figure 2 illustrates the technical approach of this study.” A new Figure 2 that illustrates the flow chart of the research approach of this study. We added a new Table 1 to summarize and describe the data used in this study. Under the 2.3 Estimating NPP with CASA Model We added additional descriptions, which read as: “The basic calculation formula of the CASA model is as follows [21]: NPP(x,t)=SOL(x,t)×FPAR(x,t)×0.5×T_ε1 (x,t)× T_ε2 (x,t)×W_ε (x,t)×ε_max (1) where SOL(x,t) is the total solar radiation at pixel x for month t. FPAR(x,t) is the fraction of photosynthetically active radiation absorbed by vegetation. 0.5 indicates the proportion of solar active radiation (0.4-0.7μm) that can be utilized by vegetation to the total solar radiation. Tε1(x,t) and Tε2(x,t) represent temperature stress coefficients, Wε(x,t) is the coefficient of water stress, and εmax is the maximum light use efficiency under ideal condition. [22]. Comparison between the calculated NPP and the reported study conducted in Northeast China and the western part of Jilin Province [23,24] as the cross checking and validation of the analysis. Mao et al. verified the NPP by comparing the simulated value with the flux observation data. The simulated value is close to the measured value, and the error is within 25% [23].” Under 2.5. Extraction of Land Surface Phenology Metrics, we added additional description, which read as: “We used the dynamic threshold method to extract metrics of LSP. The polynomial method was used to fit and reconstruct the NDVI time series data from 2000 to 2016. The software TIMESAT with a seasonal parameter of 0.5, an adaptation strength of 2.0, a Savitzky–Golay window size of 2, and an amplitude of 20% was run in MATLAB R2015b (The Mathworks, Inc., Natick, MA, USA). ” Under 2.6. Statistical Analysis, we added additional description, which read as: “Spatial trend of CUE was examined by applying a linear regression model with time as the independent variable and CUE as the dependent variables, respectively. Trend analysis method was used to analyze trend in seasonal CUE changes for the period 2001-2015. The outputs of the trend analysis are the maps of regression slope values, expressed by the following formula [19]: Slope=(n×∑_(i=1)^n▒〖i×A_i-∑_(i=1)^n▒i ∑_(i=1)^n▒A_i 〗)/(n×∑_(i=1)^n▒i^2 -(∑_(i=1)^n▒i)^2 ) (3) where Slope is the slope of the fitted regression line at each pixel. n represents year range. i is 1 for the first year, 2 for the second year, and so on. Ai represents the CUE of the year i. A negative regression coefficient (Slope < 0) indicates a decline of CUE, whereas a positive value (Slope > 0) depicts an increase trend. F test was used to determine the significance of change trend. To investigate the role of climate drivers and phenological factors affecting CUE, we analyzed the correlation between three phenological parameters (i.e. SOS, EOS and LOS) and CUE. In addition, Spearman partial correlation between CUE and two climate factors (i.e. precipitation and temperature) was also calculated. The correlation coefficient and partial correlation coefficient were computed as follows [18]: r_BC=(∑_(i=1)^n▒(B_i-B ̅ ) (C_i-C ̅ ))/(√(∑_(i=1)^n▒(B_i-B ̅ )^2 ) √(∑_(i=1)^n▒(C_i-C ̅ )^2 )) (4) B ̅=1/n ∑_(i=1)^n▒〖B_i ,〗 C ̅=1/n ∑_(i=1)^n▒〖C_i 〗 (5) r_(BC,D)=(r_BC-r_BD r_CD)/(√(1-r_BD^2 ) √(1-r_CD^2 )) (6) where rBC represents the correlation coefficient between B and C, its threshold ranges from -1 to 1, and rBC,D is the partial correlation coefficient between B and C when we controlled D values. If r < 0, B is negatively correlated with C. If r > 0, there is a positive correlation between B and C. Furthermore, B ̅,C ̅ represent the average values of Bi and Ci, respectively. The significance of the results was examined by t-test.” Point 3: The Results, you need to start with general conclusions and then to present details. Response 3: We appreciate the suggestion. We added a new paragraph in the beginning of the Result section, which reads as: “This study explained spatial patterns of ecosystem CUE at different temporal scales in a semi-humid and semi-arid transitional area. We identified that the variations of CUE in SNP were obvious at either seasonal or monthly scales. The CUE of GRA in the southwest and DBF in the east showed an upward trend. Monthly and seasonal CUE varied with ecosystem types. The earlier SOS, later EOS and longer LOS might encourage higher CUE. Spatially, CUE changes were positively correlated to precipitation and temperature in most of the SNP.” We also added additional interpretations to the figures to increase the clarity of the presentation. Point 4: The Discussion needs a significant improvements. The most important issue is to compare the most important results with outcomes of other researchers. Response 4: We appreciate the comments. We added a new Table 4, which compared the estimated CUEs between this and other reported studies. 2. We added additional descriptions, which read as: Lines 359-365: “CUE was considered as a constant value regardless of ecosystem types or species [28, 29]. However, this assumption at global scale might be controversial because it ignored the influence of environmental factors [30, 31]. Tang et al. estimated global average CUE using site data, which varied widely from 0.201 to 0.822 [3]. In this study, the estimated monthly CUE from satellite observations ranged from 0.021 to 0.999 in the SNP. The results suggested that CUE among ecosystems could not be a constant. The assumption of a constant CUE of 0.5 might lead to biased estimates for carbon cycling modelling across temporal-spatial scales.” Lines 366-369: “We compared the CUE that calculated by the same model of different ecosystems at the annual scale from other reported studies (Table 4). The order of annual CUE of different ecosystems in SNP was as follows: GRA (0.567) > WET (0.542) > MF (0.480) > DBF (0.479) [19]. However, Tang et al. found the largest CUE for WET on a global scale [3].”

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Dear Authors,

thank you very much for your improvement. The manuscript looks much better, there are still some technical errors, please, look at my comments attached to the manuscript.

Have a nice set of citations!

All the best to you

Reviewer

Comments for author File: Comments.pdf

Author Response

Dear Reviewer,

We appreciate the comments. We made sincere efforts to revise the manuscript and hope that the concerns, critiques and suggestions have been addressed to satisfaction in the revised manuscript. Please see the attachment.

All the best to you.

Authors

Author Response File: Author Response.pdf